Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model

Frozen and unfrozen surfaces exhibit different longwave surface emissivities with different spectral characteristics, and outgoing longwave radiation and cooling rates are reduced for unfrozen scenes relative to frozen ones. Here physically realistic modeling of spectrally resolved surface emissivity throughout the coupled model components of the Community Earth System Model (CESM) is advanced, and implications for model high‐latitude biases and feedbacks are evaluated. It is shown that despite a surface emissivity feedback amplitude that is, at most, a few percent of the surface albedo feedback amplitude, the inclusion of realistic, harmonized longwave, spectrally resolved emissivity information in CESM1.2.2 reduces wintertime Arctic surface temperature biases from −7.2 ± 0.9 K to −1.1 ± 1.2 K, relative to observations. The bias reduction is most pronounced in the Arctic Ocean, a region for which Coupled Model Intercomparison Project version 5 (CMIP5) models exhibit the largest mean wintertime cold bias, suggesting that persistent polar temperature biases can be lessened by including this physically based process across model components. The ice emissivity feedback of CESM1.2.2 is evaluated under a warming scenario with a kernel‐based approach, and it is found that emissivity radiative kernels exhibit water vapor and cloud cover dependence, thereby varying spatially and decreasing in magnitude over the course of the scenario from secular changes in atmospheric thermodynamics and cloud patterns. Accounting for the temporally varying radiative responses can yield diagnosed feedbacks that differ in sign from those obtained from conventional climatological feedback analysis methods.Plain Language SummaryClimate models have exhibited a persistent cold‐pole bias, whereby they systematically underestimate the average temperature and the amplification of climate change at high latitudes. A number of different explanations have been advanced for cold‐pole biases, which can be broadly divided into radiative and dynamic explanations. Here we explore in detail a relatively novel radiative explanation for the cold‐pole bias: the ice emissivity feedback. Similar to the difference in shortwave reflectivity of unfrozen and frozen surfaces, recent literature has shown that unfrozen surfaces are less emissive than frozen surfaces, which can induce a positive radiative feedback. We first present the highly nontrivial implementation of this feedback in a global circulation model (GCM) and show how to harmonize the disjointed representation of surface emissivity within the radiative transfer calculated by atmospheric and land components of a GCM. With this modified model, we show how this ice emissivity feedback depends on atmospheric water vapor and thus varies on time scales ranging from seasonal to centennial. We also show that the ice emissivity feedback is seasonally complementary to the well‐known ice‐albedo feedback, where the former is most influential during polar night. Finally, we show that including this feedback essentially eliminates the cold‐pole bias on the model we used.Key PointsLW spectral surface emissivity improves CESM Arctic surface temperature bias by 6.1 ± 1.9 degrees KelvinSpectral emissivity kernels computed for 200+ period are nonlinear in timeTemporally and spatially localized atmospheric dynamics show decreased climatological seasonal sea ice emissivity radiative response in ArcticPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142486/1/jgrd54377_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142486/2/jgrd54377.pd

Design and Implementation of a Wireless Sensor Network-Based Remote Water-Level Monitoring System

The proposed remote water-level monitoring system (RWMS) consists of a field sensor module, a base station module, adata center module and aWEB releasing module. It has advantages in real time and synchronized remote control, expandability, and anti-jamming capabilities. The RWMS can realize real-time remote monitoring, providing early warning of events and protection of the safety of monitoring personnel under certain dangerous circumstances. This system has been successfully applied in Poyanghu Lake. The cost of the whole system is approximately 1,500 yuan (RMB)

Antiproliferation and cell apoptosis inducing bioactivities of constituents from Dysosma versipellis in PC3 and Bcap-37 cell lines

<p>Abstract</p> <p>Background</p> <p>Recently, interest in phytochemicals from traditional Chinese medicinal herbs with the capability to inhibit cancer cells growth and proliferation has been growing rapidly due to their nontoxic nature. <it>Dysosma versipellis </it>as Bereridaceae plants is an endemic species in China, which has been proved to be an important Chinese herbal medicine because of its biological activity. However, systematic and comprehensive studies on the phytochemicals from <it>Dysosma versipellis </it>and their bioactivity are limited.</p> <p>Results</p> <p>Fifteen compounds were isolated and characterized from the roots of <it>Dysosma versipellis</it>, among which six compounds were isolated from this plant for the first time. The inhibitory activities of these compounds were investigated on tumor cells PC3, Bcap-37 and BGC-823 <it>in vitro </it>by MTT method, and the results showed that podophyllotoxone (PTO) and 4'-demethyldeoxypodophyllotoxin (DDPT) had potent inhibitory activities against the growth of human carcinoma cell lines. Subsequent fluorescence staining and flow cytometry analysis indicated that these two compounds could induce apoptosis in PC3 and Bcap-37 cells, and the apoptosis ratios reached the peak (12.0% and 14.1%) after 72 h of treatment at 20 <it>μ</it>M, respectively.</p> <p>Conclusions</p> <p>This study suggests that most of the compounds from the roots of <it>D. versipellis </it>could inhibit the growth of human carcinoma cells. In addition, PTO and DDPT could induce apoptosis of tumor cells.</p

Intelligent gold nanocluster for effective treatment of malignant tumor via tumor-specific photothermal-chemodynamic therapy with AIE guidance.

Precise and efficient therapy of malignant tumors is always a challenge. Herein, gold nanoclusters co-modified by aggregation-induced-emission (AIE) molecules, copper ion chelator (acylthiourea) and tumor-targeting agent (folic acid) were fabricated to perform AIE-guided and tumor-specific synergistic therapy with great spatio-temporal controllability for the targeted elimination and metastasis inhibition of malignant tumors. During therapy, the functional gold nanoclusters (AuNTF) would rapidly accumulate in the tumor tissue due to the enhanced permeability and retention effect as well as folic acid-mediated tumor targeting, which was followed by endocytosis by tumor cells. After that, the overexpressed copper ions in the tumor cells would trigger the aggregation of these intracellular AuNTF via a chelation process that not only generated the photothermal agent in situ to perform the tumor-specific photothermal therapy damaging the primary tumor, but also led to the copper deficiency of tumor cells to inhibit its metastasis. Moreover, the copper ions were reduced to cuprous ions along with the chelation, which further catalysed the excess H2O2 in the tumor cells to produce cytotoxic reactive oxygen species, resulting in additional chemodynamic therapy for enhanced antitumor efficiency. The aggregation of AuNTF also activated the AIE molecules to present fluorescence, which not only imaged the therapeutic area for real-time monitoring of this tumor-specific synergistic therapy, but also allowed us to perform near-infrared radiation at the correct time point and location to achieve optimal photothermal therapy. Both in vitro and in vivo results revealed the strong tumor elimination, effective metastasis inhibition and high survival rate of tumor-bearing mice after treatment using the AuNTF nanoclusters, indicating that this AIE-guided and tumor-specific synergistic strategy could offer a promising approach for tumor therapy

Design, synthesis and antifungal activity of novel 1,4-benzoxazin-3-one derivatives containing an acylhydrazone moiety

A series of 1,4-benzoxazin-3-one derivatives containing an acylhydrazone moiety were designed, synthesized and evaluated for their in vitro antifungal activities against Gibberella zeae, Pellicularia sasakii, Phytophthora infestans, Capsicum wilt, and Phytophthora capsica. The structures of target compounds were characterized by 1H NMR, 13H NMR, 19F NMR and HRMS. The preliminary antifungal evaluation of all target compounds showed that some target compounds possessed moderate to good activities against G. zeae, P. sasakii, P. infestans and C. wilt. Among them, compounds 5L and 5o exhibited noticeable inhibition effects against G. zeae with the EC50 values (effective concentration for 50% activity) of 20.06 and 23.17 μg/ml, respectively, which were even nearly double effective than that of hymexazol (40.51 μg/ml). Meanwhile, compound 5q displayed a notable inhibitory effect toward P. sasakii, with the EC50 value of 26.66 μg/ml, which was better than that of hymexazol (32.77 μg/ml). In addition, compound 5r yielded the EC50 value of 15.37 μg/ml against P. infestans, which was less than those of hymexazol (18.35 μg/ml) and carbendazim (34.41 μg/ml). Eventually, compound 5p showed higher inhibitory effect against C. wilt, with EC50 value of 26.76 μg/ml, which was better than that of hymexazol (&gt;50 μg/ml)

Spectrally Dependent CLARREO Infrared Spectrometer Calibration Requirement for Climate Change Detection

Detecting climate trends of atmospheric temperature, moisture, cloud, and surface temperature requires accurately calibrated satellite instruments such as the Climate Absolute Radiance and Reflectivity Observatory (CLARREO). Wielicki et al. have studied the CLARREO measurement requirements for achieving climate change accuracy goals in orbit. Our study further quantifies the spectrally dependent IR instrument calibration requirement for detecting trends of atmospheric temperature and moisture profiles. The temperature, water vapor, and surface skin temperature variability and the associated correlation time are derived using Modern Era Retrospective-Analysis for Research and Applications (MERRA) and European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The results are further validated using climate model simulation results. With the derived natural variability as the reference, the calibration requirement is established by carrying out a simulation study for CLARREO observations of various atmospheric states under all-sky. We derive a 0.04 K (k=2, or 95% confidence) radiometric calibration requirement baseline using a spectral fingerprinting method. We also demonstrate that the requirement is spectrally dependent and some spectral regions can be relaxed due to the hyperspectral nature of the CLARREO instrument. We further discuss relaxing the requirement to 0.06 K (k=2) based on the uncertainties associated with the temperature and water vapor natural variability and relatively small delay in time-to-detect for trends relative to the baseline case. The methodology used in this study can be extended to other parameters (such as clouds and CO2) and other instrument configurations

Vehicle global 6-DoF pose estimation under traffic surveillance camera

Abstract(#br)Accurately sensing the global position and posture of vehicles in traffic surveillance videos is a challenging but valuable issue for future intelligent transportation systems. Although in recent years, deep learning has brought about major breakthroughs in the six degrees of freedom (6-DoF) pose estimation of objects from monocular images, accurate estimation of the geographic 6-DoF poses of vehicles using images from traffic surveillance cameras remains challenging. We present an architecture that computes continuous global 6-DoF poses throughout joint 2D landmark estimation and 3D pose reconstruction. The architecture infers the 6-DoF pose of a vehicle from the appearance of the image of the vehicle and 3D information. The architecture, which does not rely on intrinsic camera parameters, can be applied to all surveillance cameras by a pre-trained model. Also, with the help of 3D information from the point clouds and the 3D model itself, the architecture can predict landmarks with few and/or blurred textures. Moreover, because of the lack of public training datasets, we release a large-scale dataset, ADFSC, that contains 120 K groups of data with random viewing angles. Regarding both 2D and 3D metrics, our architecture outperforms existing state-of-the-art algorithms in vehicle 6-DoF estimation

The Effects of ATIR Blocker on the Severity of COVID-19 in Hypertensive Inpatients and Virulence of SARS-CoV-2 in Hypertensive hACE2 Transgenic Mice.

Angiotensin-converting enzyme 2 (ACE2) is required for the cellular entry of the severe acute respiratory syndrome coronavirus 2. ACE2, via the Ang-(1-7)-Mas-R axis, is part of the antihypertensive and cardioprotective effects of the renin-angiotensin system. We studied hospitalized COVID-19 patients with hypertension and hypertensive human(h) ACE2 transgenic mice to determine the outcome of COVID-19 with or without AT1 receptor (AT1R) blocker treatment. The severity of the illness and the levels of serum cardiac biomarkers (CK, CK-BM, cTnI), as well as the inflammation markers (IL-1, IL-6, CRP), were lesser in hypertensive COVID-19 patients treated with AT1R blockers than those treated with other antihypertensive drugs. Hypertensive hACE2 transgenic mice, pretreated with AT1R blocker, had increased ACE2 expression and SARS-CoV-2 in the kidney and heart, 1 day post-infection. We conclude that those hypertensive patients treated with AT1R blocker may be at higher risk for SARS-CoV-2 infection. However, AT1R blockers had no effect on the severity of the illness but instead may have protected COVID-19 patients from heart injury, via the ACE2-angiotensin1-7-Mas receptor axis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12265-021-10147-3

Comparative physiological, metabolomic, and transcriptomic analyses reveal mechanisms of apple dwarfing rootstock root morphogenesis under nitrogen and/or phosphorus deficient conditions

Nitrogen (N) and phosphorus (P) are essential phytomacronutrients, and deficiencies in these two elements limit growth and yield in apple (Malus domestica Borkh.). The rootstock plays a key role in the nutrient uptake and environmental adaptation of apple. The objective of this study was to investigate the effects of N and/or P deficiency on hydroponically-grown dwarfing rootstock ‘M9-T337’ seedlings, particularly the roots, by performing an integrated physiological, transcriptomics-, and metabolomics-based analyses. Compared to N and P sufficiency, N and/or P deficiency inhibited aboveground growth, increased the partitioning of total N and total P in roots, enhanced the total number of tips, length, volume, and surface area of roots, and improved the root-to-shoot ratio. P and/or N deficiency inhibited NO3 − influx into roots, and H+ pumps played a important role in the response to P and/or N deficiency. Conjoint analysis of differentially expressed genes and differentially accumulated metabolites in roots revealed that N and/or P deficiency altered the biosynthesis of cell wall components such as cellulose, hemicellulose, lignin, and pectin. The expression of MdEXPA4 and MdEXLB1, two cell wall expansin genes, were shown to be induced by N and/or P deficiency. Overexpression of MdEXPA4 enhanced root development and improved tolerance to N and/or P deficiency in transgenic Arabidopsis thaliana plants. In addition, overexpression of MdEXLB1 in transgenic Solanum lycopersicum seedlings increased the root surface area and promoted acquisition of N and P, thereby facilitating plant growth and adaptation to N and/or P deficiency. Collectively, these results provided a reference for improving root architecture in dwarfing rootstock and furthering our understanding of integration between N and P signaling pathways

A predictive model in patients with chronic hydrocephalus following aneurysmal subarachnoid hemorrhage: a retrospective cohort study

ObjectiveOur aim was to develop a nomogram that integrates clinical and radiological data obtained from computed tomography (CT) scans, enabling the prediction of chronic hydrocephalus in patients with aneurysmal subarachnoid hemorrhage (aSAH).MethodA total of 318 patients diagnosed with subarachnoid hemorrhage (SAH) and admitted to the Department of Neurosurgery at the Affiliated People’s Hospital of Jiangsu University between January 2020 and December 2022 were enrolled in our study. We collected clinical characteristics from the hospital’s medical record system. To identify risk factors associated with chronic hydrocephalus, we conducted both univariate and LASSO regression models on these clinical characteristics and radiological features, accompanied with penalty parameter adjustments conducted through tenfold cross-validation. All features were then incorporated into multivariate logistic regression analyses. Based on these findings, we developed a clinical-radiological nomogram. To evaluate its discrimination performance, we conducted Receiver Operating Characteristic (ROC) curve analysis and calculated the Area Under the Curve (AUC). Additionally, we employed calibration curves, and utilized Brier scores as an indicator of concordance. Additionally, Decision Curve Analysis (DCA) was performed to determine the clinical utility of our models by estimating net benefits at various threshold probabilities for both training and testing groups.ResultsThe study included 181 patients, with a determined chronic hydrocephalus prevalence of 17.7%. Univariate logistic regression analysis identified 11 potential risk factors, while LASSO regression identified 7 significant risk factors associated with chronic hydrocephalus. Multivariate logistic regression analysis revealed three independent predictors for chronic hydrocephalus following aSAH: Periventricular white matter changes, External lumbar drainage, and Modified Fisher Grade. A nomogram incorporating these factors accurately predicted the risk of chronic hydrocephalus in both the training and testing cohorts. The AUC values were calculated as 0.810 and 0.811 for each cohort respectively, indicating good discriminative ability of the nomogram model. Calibration curves along with Hosmer-Lemeshow tests demonstrated excellent agreement between predicted probabilities and observed outcomes in both cohorts. Furthermore, Brier scores (0.127 for the training and 0.09 for testing groups) further validated the predictive performance of our nomogram model. The DCA confirmed that this nomogram provides superior net benefit across various risk thresholds when predicting chronic hydrocephalus. The decision curve demonstrated that when an individual’s threshold probability ranged from 5 to 62%, this model is more effective in predicting the occurrence of chronic hydrocephalus after aSAH.ConclusionA clinical-radiological nomogram was developed to combine clinical characteristics and radiological features from CT scans, aiming to enhance the accuracy of predicting chronic hydrocephalus in patients with aSAH. This innovative nomogram shows promising potential in assisting clinicians to create personalized and optimal treatment plans by providing precise predictions of chronic hydrocephalus among aSAH patients